P100. Identifying the optimal comorbidity index for assessing major complications and discharge disposition after adult spinal deformity surgery

• Published in: The spine journal 2021-09, Vol.21 (9), p.S189-S189
• Main Authors: Sachdev, Rahul, McNeely, Emmanuel, Wang, Kevin, Skolasky, Richard L., Kebaish, Khaled M., Jain, Amit, Neuman, Brian J.
• Format: Article
• Language: English
• ISSN: 1529-9430; 1878-1632
• DOI: 10.1016/j.spinee.2021.05.308

Researchers have developed an increasing number of spine-specific tools to quantify medical comorbidities and risk-stratify patients. However, ASD literature primarily utilizes the nonspecific Charlson Comorbidity Index (CCI). Identifying the optimal risk stratification tool for patients undergoing ASD surgery is critical to both patient counseling and surgical planning. This study will compare the CCI against the Seattle Spine Score (SSS), the Adult Spine Deformity-Comorbidity Score (ASD-CS), and Modified-5 Frailty Index (mFI-5) to identify the best predictor of perioperative major complications and discharge disposition after ASD surgery. This compares the predictive utility of CCI against the Seattle Spine Score (SSS), the Adult Spine Deformity-Comorbidity Score (ASD-CS), and Modified-5 Frailty Index (mFI-5) with regards to 30-day major complications and discharge disposition in the setting of ASD surgery. Retrospective review of single-institution database. A total of 164 patients undergoing ASD surgery between 2008 and 2018. Thirty-day major complications, discharge disposition. Five separate multivariable logistic regression models were created, with either 30-day major complications or discharge location as the outcome variable. The first, a base model, utilized previously validated demographic and surgical factors. The remaining four logistic regression models used the base model in conjunction with one of the four indices. The predictive value of each model was compared using goodness-of-fit testing, utilizing pseudo R² values and Akaike information criteria (AIC), with higher pseudo R² and lower AIC values indicating a model with better fit. There were 164 patients that met the inclusion and exclusion criteria. Of those, (19%) patients experienced 30-day major complications and 68 (42%) were discharged to inpatient rehabilitation (vs home). Using goodness-of-fit testing, the model utilizing SSS was found to be the best predictor of both major complications and discharge disposition (highest Pseudo R² and lowest AIC), with mFI-5 found to have similar predictive value. Models using CCI & ASD-CS were found to be inferior predictors. Models utilizing the SSS and mFI-5 were found to be better predictors of both major complications and discharge disposition. Our results suggest that the SSS and mFI-5 should be preferred over the CCI for risk stratification and outcomes research in patients undergoing ASD surgery. This abstract does not discuss or inclu